The implementation of RFID technology has been subject to ever-increasing popularity in relation to the traceability of items as one of the most advance technologies. Implementing such a technology leads to an increase in the visibility management of products. Notwithstanding this, RFID communication performance is potentially greatly affected by interference between the RFID devices. It is also subject to auxiliary costs in investment that should be considered. Hence, seeking a cost-effective design with a desired communication performance for RFID-enabled systems has become a key factor in order to be competitive in today‟s markets. This study introduce a cost and performance-effective design for a proposed RFID-enabled passport tracking system through the development of a multi-objective model that takes in account economic, operation and social criteria. The developed model is aimed at solving the design problem by (i) allocating the optimal numbers of related facilities that should be established and (ii) obtaining trade-offs among three objectives: minimising implementation and operational costs; minimising RFID reader interference; and maximising the social impact measured in the number of created jobs. To come closer to the actual design in terms of considering the uncertain parameters, a fuzzy multi-objective model was developed. To solve the multi-objective optimization problem model, two solution methods were used respectively (epsilon constrain and linear programming) to select the best Pareto solution and a decision-making method was developed to select the final trade-off solution. Moreover, this research aims to provide a user-friendly decision making tool for selecting the best vendor from a group which submitted their tenders for implementing a proposed RFID- based passport tracking system. In addition to that a real case study was applied to examine the applicability of the developed model and the proposed solution methods. The research findings indicate that the developed model is capable of presenting a design for an RFID- enabled passport tracking system. Also, the developed decision-making tool can easily be used to solve similar vendor selection problem. Research findings demonstrate that the proposed RFID-enabled monitoring system for the passport tracking system is economically feasible. The study concludes that the developed mathematical models and optimization approaches can be a useful decision-maker for tackling a number of design and optimization problems for RFID system using artificial intelligence mathematical algorithm based techniques.

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Multi-objective decision-making is critical for everyday tasks and engineering problems. Finding the perfect trade-off to maximize all the solution's criteria requires a considerable amount of experience or the availability of a significant number of resources. This makes these decisions difficult to achieve for expensive problems such as engineering. Most of the time, to solve such expensive problems, we are limited by time, resources, and available expertise. Therefore, it is desirable to simplify or approximate the problem when possible before solving it. The state-of-the-art approach for simplification is model-based or surrogate-based optimization. These approaches use approximation models of the real problem, which are cheaper to evaluate. These models, in essence, are simplified hypotheses of cause-effect relationships, and they replace high estimates with cheap approximations. In this thesis, we investigate surrogate models as wrappers for the real problem and apply \gls{moea} to find Pareto optimal decisions. The core idea of surrogate models is the combination and stacking of several models that each describe an independent objective. When combined, these independent models describe the multi-objective space and optimize this space as a single surrogate hypothesis - the surrogate compositional model. The combination of multiple models gives the potential to approximate more complicated problems and stacking of valid surrogate hypotheses speeds-up convergence. Consequently, a better result is obtained at lower costs. We combine several possible surrogate variants and use those that pass validation. After recombination of valid single objective surrogates to a multi-objective surrogate hypothesis, several instances of \gls{moea}s provide several Pareto front approximations. The modular structure of implementation allows us to avoid a static sampling plan and use self-adaptable models in a customizable portfolio. In numerous case studies, our methodology finds comparable solutions to standard NSGA2 using considerably fewer evaluations. We recommend the present approach for parameter tuning of expensive black-box functions.:1 Introduction 1.1 Motivation 1.2 Objectives 1.3 Research questions 1.4 Results overview 2 Background 2.1 Parameter tuning 2.2 Multi-objective optimization 2.2.1 Metrics for multi-objective solution 2.2.2 Solving methods 2.3 Surrogate optimization 2.3.1 Domain-specific problem 2.3.2 Initial sampling set 2.4 Discussion 3 Related Work 3.1 Comparison criteria 3.2 Platforms and frameworks 3.3 Model-based multi-objective algorithms 3.4 Scope of work 4 Compositional Surrogate 4.1 Combinations of surrogate models 4.1.1 Compositional Surrogate Model [RQ1] 4.1.2 Surrogate model portfolio [RQ2] 4.2 Sampling plan [RQ3] 4.2.1 Surrogate Validation 4.3 Discussion 5 Implementation 5.1 Compositional surrogate 5.2 Optimization orchestrator 6 Evaluation 6.1 Experimental setup 6.1.1 Optimization problems 6.1.2 Optimization search 6.1.3 Surrogate portfolio 6.1.4 Benchmark baseline 6.2 Benchmark 1: Portfolio with compositional surrogates. Dynamic sampling plan 6.3 Benchmark 2: Inner parameters 6.3.1 TutorM parameters 6.3.2 Sampling plan size 6.4 Benchmark 3: Scalability of surrogate models 6.5 Discussion of results 7 Conclusion 8 Future Work A Appendix A.1 Benchmark results on ZDT DTLZ, WFG problems